Towards Automatic Image Editing: Learning to See another You
This addresses the challenge of automatic image editing for specific object categories like faces, but it is incremental as it builds on existing deep network architectures.
The paper tackles the problem of generating plausible images with altered attributes while preserving other aspects, using a convolutional network that merges attribute information at the feature map level. It reports promising qualitative and quantitative results on face datasets like MultiPie and CAS-PEAL-R1.
Learning the distribution of images in order to generate new samples is a challenging task due to the high dimensionality of the data and the highly non-linear relations that are involved. Nevertheless, some promising results have been reported in the literature recently,building on deep network architectures. In this work, we zoom in on a specific type of image generation: given an image and knowing the category of objects it belongs to (e.g. faces), our goal is to generate a similar and plausible image, but with some altered attributes. This is particularly challenging, as the model needs to learn to disentangle the effect of each attribute and to apply a desired attribute change to a given input image, while keeping the other attributes and overall object appearance intact. To this end, we learn a convolutional network, where the desired attribute information is encoded then merged with the encoded image at feature map level. We show promising results, both qualitatively as well as quantitatively, in the context of a retrieval experiment, on two face datasets (MultiPie and CAS-PEAL-R1).